Wednesday, January 28, 2015

The snow is falling and we just measured winds clocking at over 100km/h. Par for the course, you might think, for the region of Nunavut close to the community of Cambridge Bay where we are sampling for Arctic char. Except it’s August 23rd and we didn't really plan for a wind/snow storm. An elder with us says he’s never seen anything like it in August. We’re about 85km from the closest town, in the middle of the tundra, and our kitchen tent where we usually huddle over a warm meal just flew away. Our gear and food are scattered over the tundra and my colleague and buddy Les Harris and I are running towards the sampling tent, where we’re hoping to salvage our samples. After a few minutes, Meyok, a local Inuit who helps us with our work, comes “knocking” on the tent and tells us that they need help securing shelter for the family of an Inuit hunter who is nearby and who also lost their tent. We answer that we’ll be a minute, we just have to store our samples to make sure they don’t fly away. In typical calm, no-bullshit fashion, Meyok replies: “there will be other samples – we need you”. Les and I look at each other sheepishly and instantly feel very guilty. Of course there will be other samples. We get up and we go help.

Minutes before our kitchen tent flies away scattering our gear across the tundra. Our camp was on Victoria Island, about 85km from the community of Cambridge Bay in the Canadian Arctic, where we were sampling for Arctic char. The photo was taken on August 23rd, 2013.

Hard-won data points: The day after the storm we were back out sampling. It was a little less windy, but still very cold to be playing in the water.

This story happened in 2013. It is not a “typical day” doing fieldwork in the high Arctic, but it gives an idea of the challenges we face once in a while (nobody got hurt and the samples were fine, in case you were wondering). Why, then, would someone choose to work in such difficult conditions? Most people I talk to about my work think I’m crazy for spending my summers in the Arctic: “why didn’t you choose to work on tropical fish like any sane person would do?”. I suspect that many people who read this blog, however, totally understand the thrill of doing fieldwork in a challenging, remote environment. The question for the biologist than becomes: “why did you choose a study system where it is so difficult to get data?”.

Why the Arctic?
Andrew had a really fun post recently on how he came to work on the study systems he chose. Our paths were almost diametrically opposite, but our approach – the “follow-your-nose/serendipity” approach – is surprisingly similar. So here is my own little personal story. Be warned, however, that Andrew’s version has been tried and tested, whereas I don’t have a job yet!

It was while in the Hendry lab as a Master’s student looking for a PhD project that the idea to go study Arctic fishes took form in my head. At the time, I was working on sticklebacks on northern Vancouver Island, studying the effects of gene flow on adaptive divergence in lake–stream pairs of populations. I loved my work on sticklebacks and I really enjoyed the intellectually challenging field of evolutionary ecology, but I was longing for a project with more direct applications. Fisheries work seemed interesting for a molecular ecologist, but there was already a lot of people doing great work – how was I to carve my niche there?

At the time my buddy and fellow Hendry lab-mate Nate Millar had just moved to take a job in Inuvik (funny side story: I was to learn many years later that the job became available because the aforementioned Les Harris just vacated the position – Arctic biology is a small world!). His stories of life in the North and his pictures of the Northern Lights re-kindled a long-term interest in the Arctic regions. I looked around and found out that while there was a lot of good ecology being done on Northern Canadian fishes, there wasn’t too much done in evolutionary ecology, and especially molecular ecology. I decided to start a PhD at UBC with Rick Taylor, who was happy to supervise a project on Arctic char, but had no funds to send me to the field. Thinking back, this was an incredibly risky decision. But it paid off: it turned out the gap I perceived was real, and I soon found scientists from Fisheries and Oceans Canada (DFO) that were very happy to collaborate with a molecular ecologist on a variety of projects on Arctic char – arguably the most important fish species for the Inuit of Nunavut. I was on my way to the Arctic.

And I never looked back. There is something indescribable that I love about the tundra. The rawness of the landscapes, unobstructed by trees. This feeling of extreme vulnerability once you’re out there, far from the closest town, in one of the harshest environments on earth. Doing science in the Arctic is super fun, rewarding, and a huge pain in the neck. You can’t drive to your field sites. In fact, you may have to plan 8 months in advance for a helicopter to take you there. But then, you get to ride in a helicopter over the tundra to go to your field sites. Isn’t that the sort of thing we all dreamed of when we wanted a career in biology?

And then there are the people. Working in the field with Inuit hunters has been one of the most profound and eye-opening experiences of my life. Experiencing the harshness of the climate yourself, you can’t help being amazed at these people’s survival skills – it seems crazy enough today, so imagine back in the day when they lived in igloos!! All this with a very healthy dose of humility and respect for their environment, understanding very well that they are one mistake – or one bit of bad luck – away from death. The opportunity to keep learning from Inuit hunters and to contribute in my small way to helping preserve the environment that sustains their lifestyle is now a huge motivation for me to continue my work in Nunavut.

That was the cute personal story. That’s obviously not what I tell the funding agencies. Thankfully, there are a lot of excellent reasons – scientific and practical – to be working in the Arctic. So here are, in cute baby/werewolf/silver bullet form, the “real” reasons why I work on Arctic char in the Arctic:

The Arctic char

The Arctic char (Salvelinus alpinus) is a salmonid fish and it has the northernmost distribution of all freshwater fishes. It has been used extensively as a model system in evolutionary biology because it frequently diverges into multiple sympatric ecotypes (up to four in Lake Thingvallavatn in Iceland!) in isolated post-glacial lakes where it occurs as the only species. Most of my own work, however, focuses on the anadromous form of the species (i.e., the life-history strategy whereby fish migrate between their spawning grounds in the freshwater and their feeding grounds in the saltwater).

Yours truly with a beautiful specimen of a male Arctic char in spawning colours on Baffin Island.

In Nunavut, anadromous Arctic char has been a staple of the Inuit diet for hundreds of years and remains one of the most harvested species of wildlife. Besides being an important contributor to the modern Inuit subsistence economy, in a few Nunavut communities Arctic char is also harvested commercially. These fish are transformed locally in three government-approved fish-processing plants. The fishery generates one of the few sources of income in the territory, providing employment to many fishers and plant workers to harvest a renewable resource that gives them a culturally meaningful way of participating in the cash economy.

The effective marketing campaign “Nunavut’s Truly Wild Arctic char” has created a demand in southern markets for this luxury fish. Wild-caught Arctic char are harvested in Nunavut and in some cases flown fresh to restaurants in places like Boston and San Francisco.

Commercially harvesting a resource obviously requires sound management to ensure sustainability. All indicators currently suggest that the commercial harvest of Arctic char in Nunavut is sustainable. Still, we could use a lot more biological data on the species, and much of my work has implications for the management of the fishery (more on that later). The real threat comes from the fact that the Arctic environment that is sustaining this fishery is changing. And it’s changing very fast.

The Arctic is warming up way faster than any other region on earth. This map shows 2010 temperatures compared to average temperatures in 1951–1980. Credit: NASA Goddard Institute for Space Studies.

The response of many temperate species to climate change has been to move their ranges poleward. For Arctic species, whose ranges are already limited by the pole, that won’t be an option. To avoid range contractions, and ultimately extinctions, Arctic species will have to adapt. To predict whether that will occur, and to manage to optimize opportunities for adaptation, will require a lot of work. Thankfully, the development of the eco-evo framework over the last several years provides us with a strong foundation on which to build our understanding of this multifaceted process. The central goal of my research has been to develop the empirical basis required to apply this framework to our understanding of the response of Arctic char to a changing climate.

Dispersal, gene flow, and eco-evo conservation biology of Arctic char
During my work in the Hendry lab, the main focus of my research was on the ecological and evolutionary consequences of gene flow. Naturally, this is what I decided to focus on when I started my work on Arctic char. Having spent a lot of time thinking about how gene flow can hinder local adaptation, but also fuel evolution in response to changing environments, I was very aware of the potential importance of this process for an anadromous species facing climate change.

Not knowing much at first about the complex migratory biology of the species, I did not foresee how challenging and rewarding this line of research was going to be. Like other anadromous salmonids, Arctic char tend to home to their natal streams and lakes to spawn. Contrary to other salmonids, however, char are not able to spend the winter in the saltwater. Remember Chemistry 101: saltwater has a lower freezing temperature than freshwater, meaning that the Arctic Ocean’s water is below zero under the ice during the winter. Char can’t deal with this (and with the increased salinity, but that’s another story) so they have to move back to freshwater every year. That doesn’t leave much time for feeding: in Cambridge Bay, for example, the rivers melt in late June and they start freezing again in September. For char, this means that it takes a couple of years to accumulate enough energy to build gonads, and thus they only spawn once every two or three years. The cool thing is that there were a few studies out there that suggested that char have an increased propensity to stray, or disperse, in the years when they do not spawn. I decided to test that with my favourite tool: genetic markers.

Collaborator Ross Tallman at DFO put at my disposition a large collection of tissue from adults collected from a dozen rivers around Cumberland Sound, Baffin Island, Nunavut. I wanted to assign these fish, for which we had information on reproductive status, to their rivers of origin. The idea was that returning adult fish – if the theory was right – were going to be a mix of fish from different rivers: some homing to their natal streams to spawn, others coming only to over-winter from other rivers. To assign the river of origin for these adult fish, then, I had the idea of going to the rearing lakes to sample pre-smolt juveniles that would better represent the genetic make-up of the local populations. So I got money from a federal agency to charter a helicopter to go sample juvenile char all around Cumberland Sound.

Seining for juvenile Arctic char in a remote lake on Baffin Island. Did I mention how bad the mosquitoes are?

Using these samples of juveniles as baselines, I genetically assigned the samples of adults to their most likely population of origin. When a fish was assigned to a population different from that where it was caught, I classified that individual as a stray, or a disperser. I could then test the hypothesis that fish that were not in spawning condition when caught going upriver were more likely to be classified as dispersers than fish that were going to spawn that year. And that’s exactly what we found using several different methods of assignment: there were quite a few spawners that dispersed, but non-spawners were significantly more likely to stray. You can read all about it in the paper we published on these results in the Canadian Journal of Fisheries and Aquatic Sciences here.

Mosaic plot of reproductive status vs. dispersal strategy in anadromous Arctic char from Baffin Island. Nonbreeding individuals are more likely to disperse than breeding individuals (dispersal, however, is not sex-biased). Adapted from Figure 3 in Moore et al. 2013 CJFAS.

The importance of this behaviour is potentially profound: this means that while dispersal is high, it does not necessarily translate into high gene flow. In that same paper, I used the empirical results we generated to parameterize a population genetic model showing that the reduction in gene flow increases the potential for local adaptation for these populations. I would also argue that this behaviour allows the species to benefit from some of the advantages of dispersal, including buffering adult mortality associated with unpredictable conditions during the upstream run.

As part of my postdoctoral work in the Bernatchez lab at Université Laval, I am now working to extend the precision and reach of these results in populations of anadromous char from Victoria Island, Nunavut. To do so, we are integrating next-generation sequencing data with acoustic telemetry to really tease apart the interplay between migratory behaviour, dispersal, and gene flow. The project is ongoing, but I think we will get some very important insights from combining these two powerful tools. First, we have now been collecting tracking data from Arctic char surgically implanted with acoustic tags for two summers (we’re going back for year three next summer). Those tagged fish are being tracked by an array of moored acoustic receivers that we deployed across a 120-or-so-km-long stretch of Arctic Ocean shoreline. This is a major endeavour (funded by the Ocean Tracking Network) requiring many days out on float planes, small boats, a large research vessel, and some long quad rides on the tundra to access some of the sites. But the data we’ve been getting is amazing and will teach us a lot about the fine-scale patterns of movement that Arctic char do in the Arctic Ocean. For instance, we are finding that Arctic char move back to estuaries throughout the summer – a new finding, as we thought that char spent the whole summer out in the marine environment. What’s more, they are moving in with the spring tides (i.e., when the moon is full or new) as big groups of fish mixed from several tagging locations. This mixing of stocks throughout the summer has implications for fisheries management, but also tells us that fish from different rivers use the same habitats before homing to their river of origin in the fall. Such a detailed and mechanistic understanding of migratory behaviour offers great potential to predict how patterns of dispersal, and ultimately gene flow, will change with a changing environment and how this will influence the capacity of these populations to adapt.

We are currently working to combine this tracking data with next-generation sequencing technology to genotype all tracked individuals and baseline samples from most possible source populations at thousands of SNPs. Although the microsatellite data presented earlier allowed us to test our hypothesis, there was quite a bit of uncertainty still associated with population assignment. Indeed, populations of Arctic char tend to be less genetically differentiated than other salmonids, perhaps because they only very recently recolonized their current range following the glaciation, or perhaps because they experience elevated gene flow. Anyhow, previous work by colleagues in the Bernatchez lab showed that with many thousands of SNPs, one can assign individuals to their population of origin with high precision even when genetic differentiation is weak. Our plan is therefore to assign the tagged fish to their population of origin to help better interpret patterns of movement. Since we have shown extensive mixing in the marine environment, and since we catch the fish in the summer in the marine environment, location of capture might be a poor indicator of origin. Knowing the origin of tagged fish could reveal population-specific dispersal patterns that would further increase our understanding of the potential role of gene flow in redistributing genetic variation in this system. And then, there are all the cool inferences you can make about local adaptation and gene flow from genomic data.

View of the Pangnirtung Fjord, Baffin Island, Nunavut from a helicopter. Not too difficult to imagine the glacier that snaked through this landscape just a few thousands of years ago.

How does this all fit into an “eco-evolutionary” conservation biology framework? As I wrote before, I see my work to date as putting together the building blocks of such an approach used for char, and I make no claims that my work fully encompasses the eco-evo feedback loop. But the framework guides our work. One particularly helpful concept in my opinion is that of evolutionary rescue. A lot of our work thus far has been directed at understanding how gene flow and existing genetic variation will influence genetic adaptation in the face of changing environments – the third phase of evolutionary rescue. Future work will focus on demographic resilience and on how metapopulations will persist when demographically depressed after environmental degradation – the second phase of evolutionary rescue. I think that the concept of biocomplexity and how it has been applied to salmon stocks in Alaska is particularly promising to understand resilience of Arctic char stocks. We are therefore currently planning work that will help us understand how population diversity at various scales could buffer against environmental change. As always in the Arctic, however, available data is limited, and new data is difficult to acquire...

In closing

It’s probably pretty clear from this post that I have not for one minute regretted my move to do science in the Arctic. In fact, I remain committed enough to Arctic work that I spend a lot of energy volunteering for the Association of Polar Early Career Scientists. So if you want to learn more about working in the polar regions, visit our webpage. The increased difficulty of getting data, however, means that datasets are sometimes imperfect or more limited than would be possible with model organisms that live in your back yard or are easily raised in the lab. This definitely limits the breadth of the inference we can derive, and I can’t say it’s been easy to publish in high impact journals. (But then again, is it easy for anyone?) At the same time, it’s been easier to carve myself a niche of my own, and I found that I am more satisfied with my work when I sit a little closer to the “applied” side of the “pure–applied” spectrum. It’s too soon to tell whether it will be a good move career-wise, but several signs tell me it’s not an absolute disaster either. I see a lot more interest in the Arctic regions from governments and the public, and this is starting to show in the academic world, with new journals, funding opportunities, and job offers specifically targeted at folks working in the Arctic. There have definitely been a few August mornings, as I crawled out of my down sleeping bag to put on my three layers of underwear, that I’ve asked myself “what the heck am I doing here?”. But the sight of the tundra and its endless sunrises in the late summer quickly dissipates the feeling. Or at least it does after I’ve had a cup of coffee.

Saturday, January 24, 2015

I recently
saw a tweet by @carinadslr that linked to a blog post about the “Top Ten worst graphs”.
This post provided an excellent opportunity for me to share a series of funny
scientific figures I have been collecting. I sent them out in a series of 10 tweets
and here compile them in one place.

I hope
these brighten your day and bring you a chuckle or two. Please forgive the
errors: #galapagosbandwidthsucks (as it should)

By Victor
Benno Meyer-Rochow and Jozsef Gal. (2013. Polar Biology) LINK
My favorite part of this figure is the use of a photograph of gravel juxtaposed
with the cartoonish drawing of the penguin. And you have to love the
alliteration in the title. I was pointed to this figure by a group of postdocs
and students at the Hopkins Marine Laboratory who had been collecting “best
figure 1” images. The research itself won an Ig
Nobel Prize.

2. Effects of different types of textiles on sexual activity. An experimental study.

By Ahmed Shafik. (1993. European Urology) LINK Back to absurd stand-alone figures, with another one coming from the “best figure 1” club at Hopkins Marine Lab. My favorite part here is the caption and the detailed representation of where the underpants are tied. Given the position of the ends of the string and how they seem to be lifting the underpants slightly, I can only assume that the model for this rat was drawn precisely at the moment its underpants were being tied.3. Spatial distribution of the montane unicorn.

By Stuart
H. Hurlbert. (1990. Oikos) LINK
This paper is a straight-faced use of “five populations of the recently discovered
montane unicorn” to illustrate the statistical properties of various estimators
of the spatial distribution of rare organisms. I found this figure in Steve
Heard’s wonderful paper On whimsy,
jokes, and beauty: can scientific writing be enjoyed?

By Nathalie
J. Briscoe, Kathrine A. Handasyde, Stephen R. Griffiths, Warren P. Porter,
Andrew Krockenberger, and Michael R. Kearney. (2014. Biology Letters) LINKS As
my tweets on this were coming out, @RiaRGhai sent me this one. The humor is
somewhat diminished for me by the fact that I spent an evening in an Australian
reserve looking for koalas and never saw one. That should be another category
for the figure – a bare tree branch.

5. My baby doesn’t smell as bad as yours. The plasticity
of disgust.

By Trevor
I. Case, Betty M. Repacholi, and Richard. J. Stevenson. (2006. Evolution and
Human Behavior) LINK
In this case, there isn’t anything funny about the figure itself. The humor
instead emerges when the reader mentally juxtaposes the serious presentation of
data with a mental image of the field work involved. And you have to love the “someone
else’s baby’s diaper” label.

By Michael
C. Milinkovitch, Aldagisa Caccone, and George Amato. (2004. Molecular
Phylogenetics and Evolution) In contrast to the above serious papers, here is
the first entire fake paper. Recognize the yeti drawing? Also, I read somewhere
that this paper was published on April 1 and yet it has been cited in earnest
by some people.

7. The photosynthetic cycle – CO2 dependent
transients.

By A. T.
Wilson and M. Calvin. (1955. American Chemical Society). LINK Serious paper,
serious figure, but look closely at the inset provided by Steve Heard in his
above-mentioned paper on whimsy, jokes, and beauty.

8. Fellatio by fruit bats prolongs copulation
time.

By Min Tan,
Gareth Jones, Guangjian Zhu, Jianping Ye, Tiyu Hong, Shanyi Zhou, Shuyi Zhang,
and Libiao Zhang. (2009. PLoS ONE). LINK
Figures don’t have to be static – they can be videos too. What really makes
this figure work for me is the added soundtrack (yes it is in the original paper). Apparently other papers have
now come out on cunnilingus in bats.

9. A possible role of social activity to explain
differences in publication output among ecologist.

By Tomas
Grim. (2008. Oikos) LINK
A second appearance by Oikos. Do the editors there have a better sense of humor
than elsewhere? In reality, several of the above graphs are just nods to funny
papers, rather than funny figures on their own. This papers shows how Czech
avian ecologists that drink more beer publish fewer papers and papers of lower
impact. But what is cause and what is effect?

The journal
Ecology often encourages authors to add pictures of their organisms. So, nested
with two pictures of cottonwood trees and their habitat (not shown here), is
this picture of a beaver – from Legoland!

Here is another
paper I originally tweeted that got bumped by the koala figure from my top ten
list.

By Geoffrey
Miller, Joshua M. Tybur, and Brent D. Jordan. (2007. Evolution and Human
Behavior) LINK
Like the disgust paper, the humor here doesn’t exist in the figure itself. Rather
it is in the serious presentation of the figures juxtaposed with one’s speculations
as to what the field work must have been like.

Bonus figures (post publication suggestions by readers)

Bonus 1.Predicting the distribution of Sasquatch
in western North America: anything goes
with ecological niche modelling.

By J. D. Lozier, P. Aniello, and M. J. Hickerson. (2009. Journal of Biogeography) LINK This paper brings attention to the problem of bad records in ecological niche modeling by using reported Sasquatch sightings to model the predicted range of Sasquatch in western North America. Thanks to Thiago Silva for bringing this one to my attention.

Bonus 2.

Get me off your fucking mailing list

By D. Mazieres and E. Kohler. LINK The entire papers consists only of the words "get me off your fucking mailing list." It was written as a response to the numerous spam emails the authors kept getting from the International Journal of Advanced Computer Technology. So they submitted the paper and, wonder of wonders, it was accepted. (Sadly, the journal is not actually peer-reviewed - I would have accepted it anyway though.)

Bonus 3.

By Zardoya and Meyer. LINK. We here have a straight-up molecular phylogenetics paper with a figure showing representative non-human animals and a very mis-representative human animal.

Friday, January 16, 2015

PERSERVANCE is the
hard work that you do after you get tired of doing the hard work that you
already did. (Quote on a tarp used to cover a moldering shelter found deep in
the Trinidadian bush.)

Some days everything goes just perfectly. The stars and
planets align. All the stoplights are green. All your shots go through the
hoop. All of a sudden, you are lucky in everything. Perhaps someone slipped
some Felix Felicis into your morning pumpkin juice. Of course, most other days
are a mix of lucky and unlucky, good and bad. And, every once in a while
everything goes spectacularly wrong – all at once.

Many of the best stories of wildlife photography first
describe days and weeks where everything goes wrong – or just one critical
thing goes wrong day after day after day. The bird of paradise you are watching
never displays – or never displays in your direction. A branch is always
between you and your subject no matter how you position yourself. The bird
flies away just as you raise your camera to take the picture – again and again
and again. But then one day, after weeks of perseverance, everything comes
together and you finally get that shot. Those are the good stories – hardship,
perseverance, and finally – success. By contrast, stories of easy success are boring
and stories of hardship without reward are just depressing.

Now it is a strange thing, but things that are good to have and days that are good to spend are soon told about, and not much to listen to; while things that are uncomfortable, palpitating, and even gruesome, may make a good tale, and take a deal of telling anyway.(From the Hobbit).

I am motivated to reflect on these points based on my
experiences in Panama this week. I was visiting in my role as Director of the Neotropical Environment (NEO) Graduate Option,
a partnership between the Smithsonian
Tropical Research Institute (STRI) and McGill University. While not teaching
in the class or participating in meetings about the program or hanging out with
NEO folks, my favorite activity is exploring for things to photograph –near and
far, big and small, feathered and furred and scaled and chitined. After about
ten visits over ten years, I have collected a modest but personally rewarding
collection of natural
history images

This year on the day I arrived, I immediately set out for
the last few hours of light to photograph the capybaras I knew hung out in a
small pond below the nearby Gamboa Rainforest Resort.
They were indeed there, but they were also a bit flighty and I didn’t get any useful
photos, except for some of two tiny babies that were reluctant to enter the
water. After they finally ran into the bushes, I noticed that a line of leaf
cutter ants (my favorite tropical insect) was snaking ACROSS THE SURFACE OF THE
POND, weaving its way adroitly on top of the dense aquatic vegetation while the
capybaras swam hidden below. I really wanted to get a good photo but the light
was fading fast and a huge bush made approach to the line difficult. Tomorrow, I
thought – and I will bring my GoPro on a long pole to get a video of them
crossing the pond while avoiding the brush.

Awwwww. Baby Capybaras

The next day, after a trip to see the amazing underwater
logging operation of CoastEcoTimber,
I was back with enough time and light to try again. So, quickly packing up all
my camera/video gear, I set off. Halfway there, I looked up at the canopy
tower on the hill. Hmmm, I thought, maybe I should head up there first. I might
get some good canopy bird photos in the late afternoon light and there will
probably be good leaf cutter ants on the way there, as had been the case in the
past. And maybe I can see more coatis, as I also had in the past.

By the time I neared to the top of the hill, which took some
time, I found a decent track of ants. I took out the GoPro, assembled
everything for optimal ant footage, and pushed the “on” button. Nothing. The
battery was dead. No problem, I packed two extras – at least I intended to. Yet
intent had not translated into action in my packing haste. No batteries. So I packed
everything up again and set off for the tower – at least I could still get some
good bird photos. Another five minutes of uphill hiking and I was there – but the
tower was locked. Darn, it had never been locked before. After assessing the
feasibility of climbing around the barrier, which would have been possible but
rather difficult and certainly incriminating if someone arrived, I decided to
set off for the capybaras and leaf cutters that I had seen the first day.

This time I took a different route back – a road that looked
like it was going in the right direction but that I had never gone on before. As
befit my luck, the road eventually ended without leading where I needed to go –
but then I found a path. Everything went well on the path until I reach the
bottom and realized I would have to slog through a field of thick grass that
just screamed “hellish chiggers live here.” Sure enough, I am currently
experiencing one of the itchiest chigger moments of my life. Moreover, by the
time I got back it was too late to get the GoPro battery and slog back to the
ants. Sigh. No good photos the entire evening when I had been so optimistic to
start with. (I did snap a modestly interesting photo – just to have taken a
photo of something – of a massive Nephila
spider with its parasitic Argyrodes
web-mate.)

This time at least Nephila (the big one) got its meal before Argyrodes (the little one) could steal it.

So there is my tale of just plain old bad luck (combined
with poor planning) at multiple junctures contributing to an utter failure in
my objective. Work without success. Hardship without reward. Perseverance
without redemption. OK, so I am being a melodramatic here, and maybe
“redemption” is just silly histrionics, and maybe I didn’t really
persevere that much, and maybe I did get some nice photos of other critters on
the days that followed. But I never went back to the capybaras and the leaf
cutters that walk on water. Perhaps the next trip. Then I really will have a
tale of perseverance and redemption to tell.

It's like in the great stories, Mr. Frodo. The ones that really mattered. Full of darkness and danger, they were. And sometimes you didn't want to know the end. Because how could the end be happy? How could the world go back to the way it was when so much bad had happened? But in the end, it's only a passing thing, this shadow. Even darkness must pass. A new day will come. And when the sun shines it will shine out the clearer. Those were the stories that stayed with you. That meant something, even if you were too small to understand why. (Sam in Peter Jackson’s Lord of the Rings.)

Friday, January 9, 2015

When I talk to people outside of ecology and evolutionary biology, I usually joke that my job as a graduate student is to “study fish pee”. This is intentionally self-deprecating, but it also sets up my next point: that fish pee is important and interesting. Fish pee (more formally: excretion, the release of dissolved chemicals as byproducts and excesses of metabolism) is important because it contains fertilizers (ammonium and phosphate) that can alter ecosystem function. Fish excretion is also interesting because it reflects animal physiology and can be used to assess how animal metabolism responds to varying environmental conditions. If I explain myself well, I may manage to convince my poor conversation partner that fish pee is worth studying.

While the importance of fish excretion to ecosystem function has been described by several authors (Zimmer et al. 2006; Small et al. 2011; Layman et al. 2013), much uncertainty remains as to why there is so much variation in the rates at which fish excrete. Lab studies have demonstrated that fish body temperature, body size, and food availability all cause some variation in excretion, but these factors explain just a fraction of the variation observed in empirical field studies. This unexplained variation may result from the stochastic noise caused by the stress of the experimental apparatus and difficulty of measuring nutrients, but it may also reflect variation that is induced by “cryptic determinism” due to knowable variables that have been unaccounted for in previous analyses.

Differentiating between measurement error and cryptic determinism is more than an academic exercise. If variation in excretion is due to noisy measurement methodologies, researchers will continue to struggle to predict excretion rates. In contrast, if environmental variables that we have not accounted for are causing this variation, then finding and measuring those variables will improve predictions of how much fertilizer fish will release as excretion. Describing and understanding environmental influences on excretion rates, moreover, would shed light on how evolution and phenotypic plasticity shape metabolic traits to enable survival in changing environments.

My advisor, Alex Flecker, and I undertook lab research in 2012 to assess one largely undescribed but potentially important driver of variation in fish excretion – predation risk. Our study sought to (1) understand how predation risk affects excretion by fish, and (2) explore whether these effects might reveal general adaptive responses of prey to the risk of being eaten.

Grasshoppers pee more with predators

Dror Hawlena and Os Schmitz, researchers at Yale University, motivated our work by finding that predation risk drove substantial variation in nutrient processing by a keystone invertebrate herbivore. In the old fields of Connecticut, Hawlena and Schmitz observed that the mere presence of a predatory spider increased the nitrogen (N) waste from grasshoppers. Increased grasshopper losses of N through excretion lowered the N content of their carcasses (Hawlena and Schmitz 2010a), which slowed soil respiration and decomposition in fields where predatory spiders lurked (Hawlena et al. 2012). Thus, by merely imposing risk on grasshoppers, spiders could elevate the N metabolism of their prey and alter ecosystem function. This interesting result raised the question of whether this response was general to all predators and prey, or something specific to this one empirical system.

Dror Hawlena and Os Schmitz combined their results with studies on laboratory model systems to suggest the answer is very general (Hawlena and Schmitz 2010b). Indeed, studies on laboratory animal models have repeatedly shown that exposure to predation risk increases expression of glucocorticoid steroids by prey. Elevated glucocorticoid expression results in more amino acid catabolism, which increases ammonia excretion and depletes tissue N reserves. Because the glucocorticoid response is thought to be conserved among all animals, it is possible that all animal prey excrete more nitrogen when predators lurk. Predators, then, may unlock nitrogen from their prey, driving variation in N excretion that researchers would have previously considered experimental noise.

We sought to explore this issue using another model for predator-prey interactions in nature, the Trinidadian guppy. Our study was designed to (1) explore the potential for a predator, the pike cichlid (Crenicichla sp.), to induce comparable metabolic plasticity in Trinidadian guppies, and (2) determine whether such predator-induced plasticity may be an adaptation that enhances guppy survival and reproduction in risky environments.

Our predatory fish, Crenicichla spp. This specimen was not used in this experiment, and was sent to us by an aquarium store in Portland, OR (“The Wet Spot”). It was sold to us as a “wild Crenicichla sveni”, from the Rio Orinoco in Colombia. Interestingly, its chemicals elicited the same response from guppies as did the chemicals emitted by Crenicichla sp. captured in Trinidad.

Measuring fish pee under duress

Our study was based on a design pioneered by Cameron Ghalambor, Corey Handelsman, and Emily Ruell (among others) at Colorado State University. Corey, Emily and Cameron modified complex zebrafish-rearing systems to breed and rear individual guppies from Trinidadian guppy populations. For experimentation, they varied the source of water flowing into each guppy tank, with some tanks receiving flow from a source with a single pike cichlid, and other tanks receiving flow from a source with no fish in it. Researchers in the Ghalambor lab found that the chemicals excreted by the pike cichlid consistently induced behavioral, metabolic, and life history responses in guppies (Torres-Dowdall et al. 2012; Handelsman et al. 2013).

Our zebrafish tanks, which we’ve modified to be guppy tanks as inspired by Ghalambor, Handelsman and Ruell at Colorado State.

We used a very similar design to expose 16 full-sibling groups of maturing female guppies to water either with or without the chemicals emitted by the guppies’ main diurnal predator, the pike cichlid. Over the course of seven weeks, we tracked how much N each guppy consumed in its food, how much N each retained in its tissues, and how much N each released as waste. We then used these measurements to assess how efficiently each guppy converted the N it consumed into the N in its tissue.

The “Excretionator 2000”, a device designed to enable collection of fish excretion samples with minimal invasiveness. Each basin contains water either with or without predator risk cues, pumped through each container continuously.

Fish pee less around predators?

Contrary to our expectations and the results obtained in grasshoppers by Hawlena and Schmitz, guppies reared under predator cues excreted less N than guppies reared in predator-free control water. In fact, guppies reared with predator cues excreted nearly 40% less N than controls. Largely, this difference was due to cue-exposed guppies consuming less food in the presence of the predator cue (less consumed food = less N available to excrete). Independent of differences in their food consumption or size, though, cue-exposed guppies still excreted 10% less N than control guppies. Unlike grasshoppers, which accelerated processing and excretion of N under predation risk, guppies slowed N processing and excretion under risk.

Our measurements of growth efficiency suggest there may be an adaptive benefit to the lower N excretion of predator-exposed guppies: increased growth efficiency. Cue-exposed guppies retained N more efficiently than control guppies (20% more), despite consuming less food and growing more slowly overall. It is intriguing to speculate as to whether, in the presence of predator cues, guppy metabolism changed from one maximizing growth rate – rapidly producing new tissue at the cost of high consumption and high waste production – to one maximizing growth efficiency – slowly accreting new tissue but with low rates of food consumption and waste production.

Support for the adaptive benefit of lower N excretion comes from research on the physiology of food deprivation. Animals, ranging from fish to mice to birds, disproportionately reduce their N metabolism when faced with decreased food rations (McCue 2010). This shift in metabolic fuels spares amino acids and increases catabolism of lipids, enabling starving organisms to maintain muscle mass despite limited access to dietary amino acids. Predator-exposed guppies, in this case, may be responding more strongly to the physiological challenge of food deprivation than to the direct risk imposed by predators, which would be predicted to accelerate N catabolism, excretion, and tissue depletion. We suggest two general and competing predation-related influences impact the physiology of predator-exposed prey: (1) direct risk from predators accelerates N cycling, excretion, and tissue N losses, while (2) reduced feeding under predation risk, a common behavioral change caused by predators, slows nutrient cycling and excretion, increasing N retention in tissues. (See table below; click on it to see it at larger size.)

Determining how much to pee when predators lurk

So how much should you excrete when faced with a predator? We suggest that depends on how well stocked your safe room is. If prey can shelter from predators in habitats that have abundant food resources, it may be adaptive for them to accelerate N cycling to maximize the energy available for predator encounters. The cost of their accelerated metabolism – increased loss of valuable amino acids – would be offset by the availability of amino acids in the abundant foods. If, however, prey do not have access to food in refuges, increasing N metabolism will only accelerate the negative fitness effects of starvation. For organisms facing food restriction in refuge environments, slowing N metabolism has a two-fold adaptive benefit: (1) it maintains valuable muscle protein under restricted feeding opportunity, and (2) it minimizes the amount of time spent feeding in vulnerable habitats.

In total, we found evidence that predators are central to consumer-mediated nutrient cycling, but we also found that the direction of the predator effect may depend on the environmental context of predator-prey interactions. In ecosystems where important nutrient recyclers shelter from predators in safe but food-restricted refuges, predation risk may reduce N excretion, slowing the supply of limiting nutrients to the base of the food web. Though our result runs counter to the notion of a single, general effect of predators on nutrient cycling by their prey, it also indicates that, by taking into account the natural history of predator-prey interactions, we may be able to more accurately predict how changes in predator communities will impact the function of ecosystems.

Carnival of Evolution #78 is now up. Given Bjørn’s chosen theme for this one, I think I will forego my usual custom of doing a Google Images search to find a relevant image. :->

Our contribution to this edition of the Carnival is Sarah W. Fitzpatrick’s post on Retracing the legacy of guppy introductions past. There’s lots of other good stuff in there, including an interesting post about why the claim that much of the human genome is functional implies that we should all have 7e45 children!

I hadn't initially planned a series like this, it just kind of
emerged. However, I had long planned one particular “How to” post. Ironically,
that post was the one I still hadn’t written. Now that it is 2015, the time
seems ripe to get back to the original idea. (Thanks to Ben Haller, Gregor Rolshausen, Joost Raeymaekers, and Chuck Fox for critical comments that helped improve this post.)

How to do statistics.

I used to teach statistics. Really! I was a whiz at SPSS and
Systat, and I could find my way around JMP. I was almost at the cutting edge,
which then was SAS. No one complained seriously about the stats in the papers I
submitted. Now, it seems that – with the same statistical skills as before, and
maybe even a bit better – I have become a dinosaur. Increasingly, the feeling
seems to be that you can’t be considered even moderately competent at
statistics unless you can do a GLMM in R. In this sea-change from [insert your previous
status package here] to R, I feel
that several important points are getting lost – or at least under-emphasized. My
goal in the present post is to revisit what statistics are supposed to be for
and how you should do them. I do not mean the details of how to choose and run
a particular model but rather how to view stats as a way of enhancing your
science and refining your inference. I will outline these ideas through a
series of assertions.

1. It’s all about the (appropriate)
replication

An incredibly important route to improving your science is
to maximize replication at the appropriate level of inference. Imagine
you are interested in a particular effect, say the difference in an experiment between
two treatments or the difference in some trait between populations in two
environments. You need to here strive for maximum replication of the two
treatments or the two environments. This might seem obvious but – as a
reviewer/editor – I have seen many studies where people wish to make inferences
about the effects of two environments, yet they have studied only one
population in each environment. In such cases, they are entitled to draw
conclusions about differences between the two studied populations but not
between the two environments because – with only one population per environment
– the investigator cannot gauge the difference between environments in relation
to variation within environments. That is, it is quite possible that two
populations within each environment would differ just as much as two
populations sampled from the different environments. While the temptation is to
get larger sample sizes for each measured population, what is much more
important is to sample many populations. I have seen many papers rejected for
lack of replication at the level for which inferences are desired.

2. The data are real –
statistics are merely a way of placing a statement of confidence in an
inference you draw from the data.

I have frequently seen students paralyzed by their inability
to fit an appropriate error distribution in R. They spend weeks and weeks
trying various options only to eventually give up and throw out the offending data.
The opinion seems to be that, “if I can’t fully satisfy the requirements of a
statistical test, then the data must be bad and I shouldn’t report them.” This
is folly! The data are the real thing – the stats are just a tool to aid
interpretation. What is infinitely better in cases where a perfect model cannot
be fit is to present the data, analyze them the best possible way, and then own
up to cases where the data do not fully satisfy the assumptions. The truth is
that many statistical tests are extremely robust to small-to-modest violations
of their assumptions as long as the P value (but see below) is not too close to
the critical value.

Of course, I am not here advocating using a bad model when a
better one exists. If a better model exists, by all means you should use it.
However, this more practical point is already emphasized quite frequently nowadays
to the point that it can become detrimental to a student’s progress, and I am here
trying to push the pendulum back a bit. That is, finding the ideal model is valuable
and helpful, but slavish dedication to this goal can sometimes detract from the
quality of scientific education and insight. Of course, the most important
thing is to have a good question and experimental design before you conduct the
study, which will simultaneously improve the science and help avoid later
statistical constraints.

3. It's not about the P value.

Although opinions are changing, many students are still
fixated on obtaining a P value smaller than the critical level of 0.05. This
goal is misguided – for three reasons. First, 0.05 is totally arbitrary. If you are
focused on P values, what is much more useful is the actual P value – is it small or large? (Journals should always require
actual P values in all cases.) Second, any particular set of data can be
analyzed multiple ways and cycling through those options can lead to the
temptation to choose the one that generates the smallest P value. Third, P
values themselves (the probably that, if the null hypothesis is true and you reject it, you will be wrong in doing so) are a silly way to do science – sorry RA Fisher. Among the
many reasons, the null hypothesis is – in traditional frequentist statistics – treated
as a default rather than as an alternative model, and thus one often rejects
the alternative hypothesis even when it has more support than the null
hypothesis.

Instead of null hypotheses, it is much better to specify
alternative hypotheses that are competed against each other with alternative
statistical models to thereby judge the relative support for each hypothesis.
Such comparisons can take the form of likelihood ratio tests, Bayesian
credibility intervals, AIC comparisons, or the like. One might argue that a
level of arbitrariness creeps in here (because a standard yes-no threshold is
sometimes lacking) but the truth is that such approaches are much less
arbitrary because they quantitatively
compare the level of support for competing hypotheses. The author can then draw
whatever conclusions he/she wants from the levels of support, while still
allowing the reader to draw some other conclusion from the same model
comparisons should they wish to do so.

4. Effect sizes are what
matter.

P values are determined by an interaction between effect
size (strength of an effect) and sample size. Thus, P values are NOT the
strength of an effect. As a result, one cannot – without other information – say
that a P value of 0.0001 represents a stronger effect than a P value of 0.05.
It might simply be that the former analysis has a much larger sample size. Take
simulation models as a particularly obvious example. In this case, one can have
whatever sample size one wants given computing power and time. Thus, the exact
same effect size (determined by the parameters of the simulation) can have
totally different P values determined by the number of replicate simulations
performed. If you have a tiny (but real) effect size, simply run more
simulations and it will eventually become significant! The same logic applies to
experiments and surveys. What
matters are effect sizes based on how much variance
in the data is explained, or based on the difference between group means weighted by the variance or the mean. Examples include
R2, Cohen’s D, and Eta.squared.

Of course, one still wants to place a statement of
confidence in assertions about a given effect size, which is where one adds P
values or – better yet –model comparisons as discussed above. Note that, when true
effect sizes are small, they tend to be overestimated
when sample sizes are also small, which as generates the so-called funnel
plot of meta-analyses. Thus, one still wants as large a sample size as possible
and one would ideally correct the measured effect size for an estimate of the
error – either using Bayesian approaches or through brute force. That is, a
measured R2 can be adjusted by the R2 expected if no effect was present – with
an example here.

Effect sizes (here estimates of the strength of selection) are higher when sample sizes are smaller. From Kingsolver et al. (2001 - American Naturalist).

5. Graph your data

In many meetings with students where I am to see the outcome
of their experiment or sampling for the first time, I am presented with
detailed statistical tables where the student emphasizes whether or not
particular effects are significant in this or that model. I find myself incapable
of interpreting these results without seeing the data in graphical format.
In fact, I think a student should first graph the data in a manner that
addresses the original question before running ANY formal statistical tests.
This aids not only the assessment of assumptions for subsequent statistical
tests (hugely influential outlier errors sometimes pop up when I ask a student
to do this) but also reveals – at a first glance – the gestalt effect size assessment that rarely ever changes much as time goes on, notwithstanding
any ups and downs that occur in the subsequent formal statistics. In this way,
the student and supervisor can have a rough picture of what the experiment has
revealed before having to worry about the statistical details. I would bet that
90% of the important work (if not the time investment) is done once you graph
your data in a way that informs the original hypothesis/question.

All data sets have the same means, variances, correlations, and regression lines. Only graphing shows how different they really are: Anscombe's quartet from Wikipedia.

Some additional notes
about statistical packages.

6. R is simply one of
many useful platforms for drawing statistic inference.

Nowadays, students feel incompetent if they don’t analyze
their data in R – hell, I even feel that way sometimes. However, R is simply a
post-experiment tool – a hammer with which you help massage your data into
optimal inference. SPSS, Systat, JMP, and SAS are also hammers – they too can
massage your data. Perhaps R is a titanium hammer, better and more efficient at
massaging the truth from data; but think of all the amazing inferences that were
derived before R was popular. Does the failure of these countless previous
studies to use R mean that we should not believe everything published before
(and much published after) 2002? (Of course, re-analysis does change the
conclusions of some previously-published and superficially-analyzed studies.) Does
the fact that something else will eventually replace R mean that our current
inferences with R then become incorrect? Nonsense. Valid and excellent
inference can be obtained with any number of statistical packages.

Given that R is now the most common statistical program it
does make sense for new (and old) researchers to start with (or switch to) R.
However, the main advantage is not – in my opinion – dramatically improved
inference but rather ease of communication with other scientists, such as
through the sharing of code. Moreover, R has many other components not present
in canned packages, such as data exploration
tools, connection to database and file system structures on your computer, if-else statements, while loops and other programming tools, detailed plotting
functions, connection to other programming languages such as C++ and Python
just to name a few. It also contains user-motivated novel statistical tools for
specific applications that are simply not available in other packages.

How to program a Christmas tree in R.
http://simplystatistics.org/2012/12/24/make-a-christmas-tree-in-r-with-random-ornamentspresents/

In reality, however, most scientists seek much simpler assistance
from statistical analysis, for which other packages can do the trick. Moreover,
efforts to master R can take so much time and dedication that students sometimes
neglect what is really important in science: good and novel ideas, good
experimental design, diligent execution with high replication and large sample
sizes, effective visual presentation of information, and common sense
deduction. I would much rather have a student who mastered these skills and
analyzed their data in SPSS than I would have a student who was an R whiz but neglected
the key skills of scientific investigation. Of course, what I really want is student
who can do both, but the former is vastly more important. (Of course, most
students who do learn R certainly don’t regret it afterward.)

7. R has its own foibles.

Any statistical program has bugs or flaws, and R is no
different. Many issues with existing packages have been pointed out well after
those packages were used in published studies. The simple fact is that R is
modified by many people and can (like other statistical packages) suffer from
the inadvertent introduction of errors that it takes time for others to
discover and the originators to correct. Moreover, R has its own set of
defaults that can be confusing or misleading. For instance, the standard
default in R is Type I sums of squares (SS), whereas the default in many other
stats packages in Type III SS. These different SS options have their own sets
of positives and negatives and supporters and detractors. However, one must
understand the differences between them. Of critical importance, Type I SS fits
the first term of the model first before fitting other terms, whereas Type III
SS fits all of the terms simultaneously. As a result – and as my students found
out – you can get very different results if you run the same analysis in R and
some other package, as well as if you change the order of entry of the terms in
the model in R. (For my money Type III SS is usually more appropriate and my
students now usually specify this option in R.)

It is important to make clear that I am not suggesting that
students forsake the use of R for some other package. In most cases, they
should probably use R. What I am instead saying is that learning R is not the
most important (although it could be the most useful) thing you do in your education.
Do not think that R = science and that, if you don’t learn R you are not a good
scientist. Instead, think of R as a titanium hammer. If you need that hammer,
then use it. If you don’t yet have any hammer, you might as well go titanium if
you have the time. However, remember not to equate knowledge of R with intelligence
or with a good study or with your own sense of self worth. Learn R for the
right reasons and don’t let it become your raison d’etre – unless you wish to
specialize in statistical analyses. Indeed, statistics and the development of R
packages is certainly a branch of science in its own right - but my focus in
the present post is empirical biologists who do not have a special interest in developing
statistical methods.

Coda

There are some basic thoughts about statistics that are
sometimes lost or forgotten in this brave new world of R-based statistics. The truth
is, I am not a statistics expert by any stretch of the imagination, and so I
have concentrated my comments on more basic, perhaps even philosophical,
points. However, so much training is now provided in the mechanics of
statistics, and R, that I think it is these more basic points that you are more in
danger of forgetting or foregoing. Having said all this, it is perhaps time for #SPSSHero to also become #RHero, instead of relying on my lab members to do all the heavy lifting while I simply sit around and complain about it.